This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-cased as the underlying encoder.

Model Details

Model Description

Model Type: SpanMarker
Encoder: bert-base-cased
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Training Dataset: FewNERD
Language: en
License: cc-by-sa-4.0

Model Sources

Repository: SpanMarker on GitHub
Thesis: SpanMarker For Named Entity Recognition

Model Labels

Label	Examples
art-broadcastprogram	"Street Cents", "Corazones", "The Gale Storm Show : Oh , Susanna"
art-film	"Bosch", "L'Atlantide", "Shawshank Redemption"
art-music	"Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover", "Hollywood Studio Symphony"
art-other	"Aphrodite of Milos", "Venus de Milo", "The Today Show"
art-painting	"Production/Reproduction", "Touit", "Cofiwch Dryweryn"
art-writtenart	"Imelda de ' Lambertazzi", "Time", "The Seven Year Itch"
building-airport	"Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport"
building-hospital	"Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center"
building-hotel	"The Standard Hotel", "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel"
building-library	"British Library", "Berlin State Library", "Bayerische Staatsbibliothek"
building-other	"Communiplex", "Alpha Recording Studios", "Henry Ford Museum"
building-restaurant	"Fatburger", "Carnegie Deli", "Trumbull"
building-sportsfacility	"Glenn Warner Soccer Facility", "Boston Garden", "Sports Center"
building-theater	"Pittsburgh Civic Light Opera", "Sanders Theatre", "National Paris Opera"
event-attack/battle/war/militaryconflict	"Easter Offensive", "Vietnam War", "Jurist"
event-disaster	"the 1912 North Mount Lyell Disaster", "1693 Sicily earthquake", "1990s North Korean famine"
event-election	"March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament"
event-other	"Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement"
event-protest	"French Revolution", "Russian Revolution", "Iranian Constitutional Revolution"
event-sportsevent	"National Champions", "World Cup", "Stanley Cup"
location-GPE	"Mediterranean Basin", "the Republic of Croatia", "Croatian"
location-bodiesofwater	"Atatürk Dam Lake", "Norfolk coast", "Arthur Kill"
location-island	"Laccadives", "Staten Island", "new Samsat district"
location-mountain	"Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge"
location-other	"Northern City Line", "Victoria line", "Cartuther"
location-park	"Gramercy Park", "Painted Desert Community Complex Historic District", "Shenandoah National Park"
location-road/railway/highway/transit	"Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT"
organization-company	"Dixy Chicken", "Texas Chicken", "Church 's Chicken"
organization-education	"MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College"
organization-government/governmentagency	"Congregazione dei Nobili", "Diet", "Supreme Court"
organization-media/newspaper	"TimeOut Melbourne", "Clash", "Al Jazeera"
organization-other	"Defence Sector C", "IAEA", "4th Army"
organization-politicalparty	"Shimpotō", "Al Wafa ' Islamic", "Kenseitō"
organization-religion	"Jewish", "Christian", "UPCUSA"
organization-showorganization	"Lizzy", "Bochumer Symphoniker", "Mr. Mister"
organization-sportsleague	"China League One", "First Division", "NHL"
organization-sportsteam	"Tottenham", "Arsenal", "Luc Alphand Aventures"
other-astronomything	"Zodiac", "Algol", "`` Caput Larvae ''"
other-award	"GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger"
other-biologything	"N-terminal lipid", "BAR", "Amphiphysin"
other-chemicalthing	"uranium", "carbon dioxide", "sulfur"
other-currency	"$", "Travancore Rupee", "lac crore"
other-disease	"French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer"
other-educationaldegree	"Master", "Bachelor", "BSc ( Hons ) in physics"
other-god	"El", "Fujin", "Raijin"
other-language	"Breton-speaking", "English", "Latin"
other-law	"Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act"
other-livingthing	"insects", "monkeys", "patchouli"
other-medical	"Pediatrics", "amitriptyline", "pediatrician"
person-actor	"Ellaline Terriss", "Tchéky Karyo", "Edmund Payne"
person-artist/author	"George Axelrod", "Gaetano Donizett", "Hicks"
person-athlete	"Jaguar", "Neville", "Tozawa"
person-director	"Bob Swaim", "Richard Quine", "Frank Darabont"
person-other	"Richard Benson", "Holden", "Campbell"
person-politician	"William", "Rivière", "Emeric"
person-scholar	"Stedman", "Wurdack", "Stalmine"
person-soldier	"Helmuth Weidling", "Krukenberg", "Joachim Ziegler"
product-airplane	"Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS"
product-car	"100EX", "Corvettes - GT1 C6R", "Phantom"
product-food	"red grape", "yakiniku", "V. labrusca"
product-game	"Airforce Delta", "Hardcore RPG", "Splinter Cell"
product-other	"Fairbottom Bobs", "X11", "PDP-1"
product-ship	"Congress", "Essex", "HMS `` Chinkara ''"
product-software	"AmiPDF", "Apdf", "Wikipedia"
product-train	"High Speed Trains", "55022", "Royal Scots Grey"
product-weapon	"AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II"

Uses

Direct Use

python

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

Downstream Use

You can finetune this model on your own dataset.

Click to expand

python

from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-bert-base-fewnerd-fine-super-finetuned")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Sentence length	1	24.4945	267
Entities per sentence	0	2.5832	88

Training Hyperparameters

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.9.16
SpanMarker: 1.3.1.dev
Transformers : 4.29.2
PyTorch: 2.0.1+cu118
Datasets: 2.14.3
Tokenizers: 0.13.2